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_a10.1007/978-981-15-0798-4 _2doi |
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_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
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_aRC269 _b2019 EB |
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_aISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging _bSelect Proceedings _cedited by Anubha Gupta, Ritu Gupta. |
| 250 | _aFirst edition | ||
| 264 | 1 |
_aSingapore _bSpringer International Publishing _c2019 |
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| 300 |
_a1 recurso en línea (X, 147 páginas) _b64 ilustraciones, 61 ilustraciones a color |
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| 336 |
_2rdacontent _aTexto _btxt |
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_2rdamedia _aelectrónico _bc |
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_2rdacarrier _arecurso electrónico _bcr |
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_atext file _bPDF |
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_aLecture Notes in Bioengineering _x2195-271X |
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| 490 | 0 | _aBiomedical and Life Sciences (Springer-11642) | |
| 505 | 0 | _aChapter 1: Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images -- Chapter 2: Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques -- Chapter 3: . | |
| 520 | 3 | _aThis book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment. | |
| 988 | _aPrimersemestre_2020_BiomedLife | ||
| 650 | 7 |
_2embne _9146156 _aCélulas cancerosas |
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| 700 | 1 |
_aGupta, Anubha _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aGupta, Ritu _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811507977 |
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_iPrinted edition: _z9789811507991 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811508004 |
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_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-0798-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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